To address the inadequate restoration of textures and edge details in super-resolution reconstruction of rock CT images, along with the high resource consumption of traditional Transformer models, this study proposes a lightweight hybrid architecture, the pixel difference convolution and lightweight Transformer (PDCLT) model. The model integrates a detail-enhancement convolutional neural network (CNN) module based on pixel difference convolution and a lightweight Transformer module to efficiently extract both local and global features. Specifically, the model first introduces a detail enhancement module that combines pixel difference convolution with residual enhanced attention. It also proposes an adaptive path weight scaling method to dynamically adjust the weights of feature extraction paths, which enhances the capture of subtle structures and key features. Secondly, the lightweight Transformer module incorporates efficient multi-head self-attention and a multi-scale feature fusion network to reduce GPU memory demands while extracting global and multi-scale features. Finally, porosity loss is added to the loss function to optimize the preservation of pore structures. Experimental results show that the PDCLT model excels in reconstruction quality and detail restoration, significantly improving the super-resolution reconstruction quality of rock CT images.